Parameter Identification of a Governing System in a Pumped Storage Unit Based on an Improved Artificial Hummingbird Algorithm

Wang, Liying; Zhang, Luyao; Zhao, Weiguo; Liu, Xiyuan

doi:10.3390/en15196966

Cited by 13 publications

(2 citation statements)

References 35 publications

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“…To further demonstrate the performance of the IAHA algorithm, we conduct comparative experiments with two recent improvement algorithms: CLAHA proposed by Wang et al [ 26 ] and AOAHA proposed by Ramadan et al [ 27 ]. The experimental results are presented in Table 4 .…”

Section: Our Proposed Iaha Algorithmmentioning

confidence: 99%

A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment

Feng

2023

Sensors

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Coal is an important resource that is closely related to people’s lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved.

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Section: Our Proposed Iaha Algorithmmentioning

confidence: 99%

A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment

Feng

2023

Sensors

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“…Chaos theory relates the chaotic dynamics of systems with initial conditions and unstable periodic motions [ 42 ]. It is applied in various applications such as biometric security [ 43 ], embedded systems [ 44 ], communications [ 45 ], lasers [ 46 ], pumped storage units [ 47 ], encryption systems [ 48 ], the Internet of Things [ 49 ], image processing [ 50 ], and image encryption [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

et al. 2023

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In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

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